| 1. |
- Corral-Lopez, Alberto, 1984-, et al.
(author)
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Evolution of schooling drives changes in neuroanatomy and motion characteristics across predation contexts in guppies
- 2023
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In: Nature Communications. - : Springer Nature. - 2041-1723. ; 14
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Journal article (peer-reviewed)abstract
- One of the most spectacular displays of social behavior is the synchronized movements that many animal groups perform to travel, forage and escape from predators. However, elucidating the neural mechanisms underlying the evolution of collective behaviors, as well as their fitness effects, remains challenging. Here, we study collective motion patterns with and without predation threat and predator inspection behavior in guppies experimentally selected for divergence in polarization, an important ecological driver of coordinated movement in fish. We find that groups from artificially selected lines remain more polarized than control groups in the presence of a threat. Neuroanatomical measurements of polarization-selected individuals indicate changes in brain regions previously suggested to be important regulators of perception, fear and attention, and motor response. Additional visual acuity and temporal resolution tests performed in polarization-selected and control individuals indicate that observed differences in predator inspection and schooling behavior should not be attributable to changes in visual perception, but rather are more likely the result of the more efficient relay of sensory input in the brain of polarization-selected fish. Our findings highlight that brain morphology may play a fundamental role in the evolution of coordinated movement and anti-predator behavior.
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| 2. |
- Giri, Sambit K., et al.
(author)
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Identifying reionization-epoch galaxies with extreme levels of Lyman continuum leakage in James Webb Space Telescope surveys
- 2020
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In: Monthly notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 491:4, s. 5277-5286
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Journal article (peer-reviewed)abstract
- The James Webb Space Telescope (JWST) NIRSpec instrument will allow rest-frame ultraviolet/optical spectroscopy of galaxies in the epoch of reionization (EoR). Some galaxies may exhibit significant leakage of hydrogen-ionizing photons into the intergalactic medium, resulting in faint nebular emission lines. We present a machine learning framework for identifying cases of very high hydrogen-ionizing photon escape from galaxies based on the data quality expected from potential NIRSpec observations of EoR galaxies in lensed fields. We train our algorithm on mock samples of JWST/NIRSpec data for galaxies at redshifts z = 6-10. To make the samples more realistic, we combine synthetic galaxy spectra based on cosmological galaxy simulations with observational noise relevant for z greater than or similar to 6 objects of a brightness similar to EoR galaxy candidates uncovered in Frontier Fields observations of galaxy cluster Abell-2744 and MACS-J0416. We find that ionizing escape fractions (f(esc)) of galaxies brighter than m(AB,1500) approximate to 27 mag may be retrieved with mean absolute error Delta f(esc) approximate to 0.09(0.12) for 24 h (1.5 h) JWST/NIRSpec exposures at resolution R = 100. For 24 h exposure time, even fainter galaxies (m(AB,1500) < 28.5 mag) can be processed with Delta f(esc) approximate to 0.14. This framework simultaneously estimates the redshift of these galaxies with a relative error less than 0.03 for both 24 (m(AB,1500) < 28.5 mag) and 1.5 h (m(AB,1500) < 27 mag) exposure times. We also consider scenarios where just a minor fraction of galaxies attain high f(esc) and present the conditions required for detecting a subpopulation of high-f(esc) galaxies within the data set.
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| 3. |
- Kotrschal, Alexander, et al.
(author)
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Rapid evolution of coordinated and collective movement in response to artificial selection
- 2020
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In: Science Advances. - : AMER ASSOC ADVANCEMENT SCIENCE. - 2375-2548. ; 6:49
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Journal article (peer-reviewed)abstract
- Collective motion occurs when individuals use social interaction rules to respond to the movements and positions of their neighbors. How readily these social decisions are shaped by selection remains unknown. Through artificial selection on fish (guppies, Poecilia reticulata) for increased group polarization, we demonstrate rapid evolution in how individuals use social interaction rules. Within only three generations, groups of polarization-selected females showed a 15% increase in polarization, coupled with increased cohesiveness, compared to fish from control lines. Although lines did not differ in their physical swimming ability or exploratory behavior, polarization-selected fish adopted faster speeds, particularly in social contexts, and showed stronger alignment and attraction responses to multiple neighbors. Our results reveal the social interaction rules that change when collective behavior evolves.
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| 4. |
- Pelckmans, Kristiaan
(author)
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Monitoring High-Frequency Data Streams in FinTech : FADO Versus K-means
- 2020
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In: IEEE Intelligent Systems. - 1541-1672 .- 1941-1294. ; 35:2, s. 36-42
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Journal article (peer-reviewed)abstract
- Modern applications of FinTech are challenged by enormous volumes of financial data. One way to handle these is to adopt a streaming setting where data are only available to the algorithms during a very short time. When a new data point (financial transaction) is generated, it needs to be processed directly, and be forgotten immediately after. Especially, ongoing globalization efforts in FinTech require modern methods of fault detection to be able to work efficiently through more than 10 000 financial transactions per second if they are to be deployed as a first line of defence. This article investigates two algorithms able to perform well in this demanding setting: K-means and FADO. Especially, this article provides supports for the claim that “the use of multiple clusters does not necessarily translate into increased detection performance”. To support this claim, results are reported when operating in a quasi-realistic case study of Anti Money Laundering (AML) detection in real-time payment systems. We focus on two prototypical algorithms: the passive aggressive FADO assuming a single cluster, and the well-known K-means algorithm working with K > 1 clusters. We find-in this case-that the use of K-means with multiple clusters is unfavorable as 1) both tuning for K, as well as the need for additional complexity in the K-means algorithm challenges the computational constraints; 2) K-means introduces necessarily added variability (unreliability) in the results; 3) it requires dimensionality reduction, compromising interpretability of the detections; 4) the prevalence of singleton clusters adds unreliability to the outcome. This makes in the presented case FADO favorable over K-means (with K > 1).
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| 5. |
- Souza, Abel, PhD, 1986-, et al.
(author)
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A HPC Co-scheduler with Reinforcement Learning
- 2021
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer. - 9783030882235 - 9783030882242 ; , s. 126-148
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Conference paper (peer-reviewed)abstract
- Although High Performance Computing (HPC) users understand basic resource requirements such as the number of CPUs and memory limits, internal infrastructural utilization data is exclusively leveraged by cluster operators, who use it to configure batch schedulers. This task is challenging and increasingly complex due to ever larger cluster scales and heterogeneity of modern scientific workflows. As a result, HPC systems achieve low utilization with long job completion times (makespans). To tackle these challenges, we propose a co-scheduling algorithm based on an adaptive reinforcement learning algorithm, where application profiling is combined with cluster monitoring. The resulting cluster scheduler matches resource utilization to application performance in a fine-grained manner (i.e., operating system level). As opposed to nominal allocations, we apply decision trees to model applications’ actual resource usage, which are used to estimate how much resource capacity from one allocation can be co-allocated to additional applications. Our algorithm learns from incorrect co-scheduling decisions and adapts from changing environment conditions, and evaluates when such changes cause resource contention that impacts quality of service metrics such as jobs slowdowns. We integrate our algorithm in an HPC resource manager that combines Slurm and Mesos for job scheduling and co-allocation, respectively. Our experimental evaluation performed in a dedicated cluster executing a mix of four real different scientific workflows demonstrates improvements on cluster utilization of up to 51% even in high load scenarios, with 55% average queue makespan reductions under low loads.
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| 6. |
- Souza, Abel, PhLic. 1986-, et al.
(author)
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ASA - The Adaptive Scheduling Architecture
- 2020
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In: HPDC '20: Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing. - New York, NY, USA : ACM Digital Library. - 9781450370523 ; , s. 161-165
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Conference paper (peer-reviewed)abstract
- In High Performance Computing (HPC), resources are controlled by batch systems and may not be available due to long queue waiting times, negatively impacting application deadlines. This is noticeable in low latency scientific workflows where resource planning and timely allocation are key for efficient processing. On the one hand, peak allocations guarantee the fastest possible workflows execution time, at the cost of extended queue waiting times and costly resource usage. On the other hand, dynamic allocations following specific workflow stage requirements optimizes resource usage, though it increases the total workflow makespan. To enable new scheduling strategies and features in workflows, we propose ASA: the Adaptive Scheduling Architecture, a novel scheduling method to reduce perceived queue waiting times as well as to optimize workflows resource usage. Reinforcement learning is used to estimate queue waiting times, and based on these estimates ASA pro-actively submit resource change requests, minimizing total workflow inter-stage waiting times, idle resources, and makespan. Experiments with three scientific workflows at two HPC centers show that ASA combines the best of the two aforementioned approaches, with average queue waiting time and makespan reductions of up to 10% and 2% respectively, with up to 100% prediction accuracy, while obtaining near optimal resource utilization.
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| 7. |
- Villarroel, Beatriz, et al.
(author)
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Launching the VASCO Citizen Science Project
- 2022
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In: Universe. - : MDPI AG. - 2218-1997. ; 8:11, s. 561-
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Journal article (peer-reviewed)abstract
- The Vanishing & Appearing Sources during a Century of Observations (VASCO) project investigates astronomical surveys spanning a time interval of 70 years, searching for unusual and exotic transients. We present herein the VASCO Citizen Science Project, which can identify unusual candidates driven by three different approaches: hypothesis, exploratory, and machine learning, which is particularly useful for SETI searches. To address the big data challenge, VASCO combines three methods: the Virtual Observatory, user-aided machine learning, and visual inspection through citizen science. Here we demonstrate the citizen science project and its improved candidate selection process, and we give a progress report. We also present the VASCO citizen science network led by amateur astronomy associations mainly located in Algeria, Cameroon, and Nigeria. At the moment of writing, the citizen science project has carefully examined 15,593 candidate image pairs in the data (ca. 10% of the candidates), and has so far identified 798 objects classified as "vanished". The most interesting candidates will be followed up with optical and infrared imaging, together with the observations by the most potent radio telescopes.
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| 8. |
- Villarroel, Beatriz, et al.
(author)
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The Vanishing and Appearing Sources during a Century of Observations Project. I. USNO Objects Missing in Modern Sky Surveys and Follow-up Observations of a "Missing Star"
- 2020
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In: Astronomical Journal. - : American Astronomical Society. - 0004-6256 .- 1538-3881. ; 159:1
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Journal article (peer-reviewed)abstract
- In this paper we report the current status of a new research program. The primary goal of the "Vanishing and Appearing Sources during a Century of Observations" project is to search for vanishing and appearing sources using existing survey data to find examples of exceptional astrophysical transients. The implications of finding such objects extend from traditional astrophysics fields to the more exotic searches for evidence of technologically advanced civilizations. In this first paper we present new, deeper observations of the tentative candidate discovered by Villarroel et al. in 2016. We then perform the first searches for vanishing objects throughout the sky by comparing 600 million objects from the US Naval Observatory Catalogue (USNO) B1.0 down to a limiting magnitude of similar to 20-21 with the recent Pan-STARRS Data Release-1 (DR1) with a limiting magnitude of similar to 23.4. We find about 150,000 preliminary candidates that do not have any Pan-STARRS counterpart within a 30 '' radius. We show that these objects are redder and have larger proper motions than typical USNO objects. We visually examine the images for a subset of about 24,000 candidates, superseding the 2016 study with a sample 10 times larger. We find about 100 point sources visible in only one epoch in the red band of the USNO, which may be of interest in searches for strong M-dwarf flares, high-redshift supernovae, or other categories of unidentified red transients.
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